基于仿真的低维混沌系统参数漂移测量思想实验。

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-06-01 DOI:10.1063/5.0230984
M Herein, T Tél, T Haszpra
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引用次数: 0

摘要

我们认为,涉及漂移参数的系统的物理实验需要范式转换:测量信号,曲线,应该与模拟结果的频带进行比较。基于早期的理论结果,漂移耗散混沌系统的精确描述只能通过跟踪轨迹集合来给出。在收敛到一个时间相关的吸引子(所谓的快照吸引子)之后,集合忠实地表示了动力学。我们指出,实验测量的信号应该在收敛的数值系综的扩展范围内徘徊,即表现为快照吸引子上的任何系综成员。如果是这样的话,用于模拟的模型(一组常微分方程)可以被认为是可信的。当使用两个初始局域系综时,到达吸引子之前的瞬态阶段可以分为两个阶段。在第一种情况下,群聚体迅速扩散,形成羽流图。接下来,中间阶段对应于不再局域化的系综收敛到同一唯一的依赖于时间的吸引子,并且大约持续到两个系综的平均值和其他统计矩保持不同的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simulation-supported thought experiment for measuring low-dimensional chaotic systems subjected to parameter drift.

We argue that a physics experiment with systems involving drifting parameters requires a paradigm shift: the measured signal, a curve, should be compared with a band resulting from simulations. Based on earlier theoretical results, an accurate description of drifting dissipative chaotic systems can only be given by following an ensemble of trajectories. After convergence to a time-dependent attractor (to the so-called snapshot attractor), the ensemble faithfully represents the dynamics. We point out that an experimentally measured signal should wander within the spread of the converged numerical ensemble, i.e., to behave as any of the ensemble members on the snapshot attractor. If that is the case, the model (a set of ordinary differential equations) used for the simulation can be considered credible. The transient period preceding the arrival to the attractor can be divided into two phases when using two initially localized ensembles. In the first one, a quick spread of the ensembles takes place, and a plume diagram evolves. The next, intermediate phase corresponds to a convergence of the no longer localized ensembles to the same unique time-dependent attractor and lasts approximately as long as the averages and other statistical moments of the two ensembles remain distinct.

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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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